Finding high-influence microblog users with an improved PSO algorithm Online publication date: Sat, 16-Aug-2014
by Biao Zhang; Shuai Zhong; Kunmei Wen; Ruixuan Li; Xiwu Gu
International Journal of Modelling, Identification and Control (IJMIC), Vol. 18, No. 4, 2013
Abstract: Particle swarm optimisation (PSO) is a stochastic optimisation algorithm based on swarm intelligence. The algorithm applies the concept of social interaction to find optimal solution. Sina Weibo is one of the most popular Chinese microblog platforms. Microblog users participate in network interaction by publishing tweets and retweets. The influences of microblog users are determined by the users' behaviours, which exactly match the five principles of swarm intelligence. Therefore, we propose an improved PSO algorithm to find the microblog users with the maximum influence. Microblog users' retweeting behaviours can be described as a variable of the user influence space, which contains user experiences and surrounding network. The variable is defined as the velocity change in our method. By iteratively calculating based on users' behaviour, the maximum influence will be obtained. The experiments validate that our method can effectively identify the high-influence microblog users.
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